Slides da palestra "Direto dos Laboratórios da Rakuten Japão: O Futuro do Comércio Eletrônico" feita pelo Yoichi Yoshimoto, Cientista do RIT- Instituto de Tecnologia da Rakuten.

2. Various Data
Visualize
Useful insight

3. Growing Data in Rakuten
Ichiba GMS （∝ # of transaction）
160 M
# of items # of reviews

4. …will be happening in Brazil as well
Source: eMarketer Jan 2014 “Retail Ecommerce Sales in Brazil to See Double-Digit Growth This Year”

5. Growing services with Growing
Data
Vol.01 Oct/14/2014
Yoichi Yoshimoto | Mario
Rakuten Institute of Technology, Rakuten Inc.
http://rit.rakuten.co.jp/

6. • Yoichi Yoshimoto
• Lead Coordinator
Rakuten Institute of Technology
Rakuten, Inc.
• So what’s my role?
 Connecting R&D projects to business and tech teams
 Currently focusing on “Data Mining” and “Natural
Language Processing” areas.

7. Rakuten, Inc.

8. 1997 → 2014
Our Mission: “Empowering
People and Societies through the Internet”.

9. Did you know…?

10. Key figures(1)

11. Key figures(2)

12. # of Search Requests in Rakuten Ichiba
New Year
3.11
w/o login（SP）
female（SP）
male（SP）
w/o login（PC）
female（PC）
male（PC）
(Nov. 2010 – Jun. 2014)

13. So how do we utilize this data?

14. Visualizing

16. HITOKE Application

17. Analyzing (and grasping trends)
分析

18. 【Keyword Trend】Peak Season Identification
When is the peak season for selling school bags?

19. 【Keyword Trend】Peak Season Identification
Re-discovering peak season from time series data
Jan. 1st
Dec. 31st
“School Bags” have 2 peak seasons
Grandparents start looking for
school bags as gifts for their
grandchildren after their visit
during summer vacation.
Maybe!

20. 【Keyword Trend】Discovering event related demands
Finding unknown co-relations
from temporal keyword data

21. 【Keyword Trend】Discovering event related demands
Finding unknown co-relations
from temporal keyword data

22. Source: empty - Philip Tautz

23. 【Keyword Trend】Discovering event related demands
Burst keywords after Great East Japan Earthquake
We can see demands that aren’t reflected in POS data.

24. Utilizing (for usability improvements)

25. Item Genre Suggestion
Identifying Rakuten’s genres
which are related to user-input keywords
ワンピース(one piece)

26. Item Genre Suggestion
Detecting biases in users’ search behavior
Women’s Clothing
ワンピース(one piece)
Men’s Clothing
Sports & Outdoors
Toys, Hobbies & Games
Home Appliances
Related!
Related!
Related!
Kids, Baby & Maternity

27. Organizing (to acquire knowledge)

28. Attribute Extraction
Item pages in Rakuten are created by merchants
-> They Contain lots of unstructured text.
For better service, we need structured data.
Hard to see wine’s attributes Easy to see wine’s attributes

29. Attribute Extraction
Item pages in Rakuten are created by merchants
-> They Contain lots of unstructured text.
For better service, we need structured data.
Hard to see wine’s attributes Easy to see wine’s attributes

30. Attribute Extraction
We can extract attributes regardless of categories or
languages as long as table data is available

31. Further Analyzing

32. GEAP: Global Event Analysis Platform
• Collecting log data from any devices and services
with single platform.
• Big data analysis of cross services